计算机科学 ›› 2018, Vol. 45 ›› Issue (9): 237-242.doi: 10.11896/j.issn.1002-137X.2018.09.039

• 人工智能 • 上一篇    下一篇

基于网络回复的律师评价方法

杨开平, 李明奇, 覃思义   

  1. 电子科技大学数学科学学院 成都611731
  • 收稿日期:2017-08-18 出版日期:2018-09-20 发布日期:2018-10-10
  • 通讯作者: 李明奇(1970-),副教授,主要研究方向为信息论及其应用,E-mail:lmqi2000@126.com
  • 作者简介:杨开平(1990-),男,硕士,主要研究方向为自然语言处理、数据挖掘;覃思义(1971-),副教授,主要研究方向为时间序列分析及其应用。
  • 基金资助:
    本文受国家自然科学基金面上项目(61571097)资助。

Lawyer Evaluation Method Based on Network Response

YANG Kai-ping, LI Ming-qi, QIN Si-yi   

  1. School of Mathematical Sciences,University of Electronic Science and Technology of China,Chengdu 611731,China
  • Received:2017-08-18 Online:2018-09-20 Published:2018-10-10

摘要: 随着社会与互联网的不断发展,公民的法律意识越来越强,传统的律师业务流程与发展模式已经不能满足客户和行业的需求。根据已有的专业律师咨询回复规范,文中建立了判定回复信息质量优劣的准则,并从5个方面对回复文本进行了量化描述。利用word2vec算法对律师问答系统的历史数据库进行训练,得到该数据库的词向量和对应词语的相似度。基于词语相似度和文本长度,构造文本间相似度。由此,建立了律师回复信息质量评价模型。对数据库中各个律师的问答文本进行了量化分析,结果表明,该模型能够很好地评估律师的回复质量。

关键词: word2vec, 回复质量, 网络回复, 语义相似度

Abstract: With the development of society and the Internet,the citizen’s legal consciousness is gradually raised.Hence,the traditional business process and developmodels for lawyers are unsuitable for customers as well as the industry.Based on the existing response standards of professional lawyer consultation,the criteria for judging the response quality was proposed in this paper.Moreover,the response texts were quantitatively described from 5 aspects.Based on the word2vec algorithm,the similarity between word vectors and corresponding words was obtained from the existing database of lawyer question and answer system.Furthermore,the similarity function of texts was proposed based on word similarity and text length.Consequently,the quality evaluation model of the response of lawyers was established.Simulations were given to verify the validity of the model.The results show that the proposed model works well in evaluating the response quality of lawyer after the quantitative analysis of the question and answer text of each lawyer in the database.

Key words: Network response, Response quality, Semantic similarity, Word2vec

中图分类号: 

  • TP391
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